options(scipen=999, warn = -1)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(plotly)
##
## Attaching package: 'plotly'
##
## The following object is masked from 'package:ggplot2':
##
## last_plot
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following object is masked from 'package:graphics':
##
## layout
courses <- as_tibble(read.csv("C:/Users/crane/Documents/MA342/Project/courses.csv"))
institutions <- read.csv("C:/Users/crane/Documents/MA342/Project/Institutions.csv")
#remove special characters in tuition, fees, placement, and wage columns
#forcing columns as numeric replaces the ^ and - characters present in data set to NA, which is what we want
courses <- courses %>%
mutate(inst_id = as.numeric(inst_id)) %>%
mutate(AnnualTuition = as.numeric(str_replace(AnnualTuition, "[$%]", ""))) %>%
mutate(AnnualFees = as.numeric(str_replace(AnnualFees, "[$%]", ""))) %>%
mutate(Placement = as.numeric(str_replace(Placement, "[$%]", ""))) %>%
mutate(MedWageEntry = as.numeric(str_replace(MedWageEntry, "[$%]", ""))) %>%
mutate(MedWage5yr = as.numeric(str_replace(MedWage5yr, "[$%]", "")))
#add columns for total cost per year
courses <- courses %>%
mutate(AnnualCost = AnnualTuition + AnnualFees) %>%
mutate(TotalEstimatedCost = (RequiredHours/30)*AnnualCost) %>%
mutate(Med5YrValue = MedWage5yr/TotalEstimatedCost) %>%
mutate(MedEntryValue = MedWageEntry/TotalEstimatedCost)
#__________________________________________________________________________________________________
#plot showing cost of degree vs median entry wage of all degrees, color by required hours
#these two plots best answer the question, what degree from KHE schools have the best value
#both of these plots are interactive, meaning they show additional information when you
#hover over one of the data points but must be viewed as an html file
plt1 <- courses %>%
left_join(institutions, by = "inst_id") %>%
filter(TotalEstimatedCost < 75000) %>%
ggplot() +
geom_point(mapping = aes(x = TotalEstimatedCost, y = MedWageEntry, color = RequiredHours, text = paste0(DegreeTitle, " ", InstitutionName))) +
ggtitle("Total Cost versus Median Entry Level Salary")
ggplotly(plt1)
plt2 <- courses %>%
left_join(institutions, by = "inst_id") %>%
filter(TotalEstimatedCost < 75000) %>%
ggplot() +
geom_point(mapping = aes(x = TotalEstimatedCost, y = MedWage5yr, color = RequiredHours, text = paste0(DegreeTitle, " ", InstitutionName))) +
ggtitle("Total Cost versus Median Salary After 5 years")
ggplotly(plt2)
plt3 <- courses %>%
left_join(institutions, by = "inst_id") %>%
ggplot()+
geom_point(mapping = aes(x = TotalEstimatedCost, y = Placement, color = RequiredHours, text = paste0(DegreeTitle, " ", InstitutionName))) +
ggtitle("Total Cost vs Placement%")
ggplotly(plt3)
#Using the results of these 3 charts, we can make a determination that associates degrees
#have the highest value in terms of cost vs wage and placement percentages
#its also interesting to note that the points are in 3 distinct groups based on cost
#1) two year colleges <20000
#2) Fort Hayes, Pittsburg, Emporia >20000 and < 40000
#3) KU, KSU, WSU, WU >40000
#obviously an associates degree isn't not for everyone, so below is a graph of just the bachelors degrees
plt4 <- courses %>%
left_join(institutions, by = "inst_id") %>%
filter(RequiredHours >= 120) %>%
ggplot() +
geom_point(mapping = aes(x = TotalEstimatedCost, y = MedWage5yr, color = RequiredHours, text = paste0(DegreeTitle, " ", InstitutionName))) +
ggtitle("Total Cost versus Median Salary After 5 years (Bachelors Degrees)")
ggplotly(plt4)
#___________________________________________________________________________________________________
#plot showing distribution of wages by university
courses %>%
left_join(institutions, by = "inst_id") %>%
filter(inst_type %in% c("State University", "Municipal University")) %>%
ggplot() +
geom_boxplot(mapping = aes(x = InstitutionName, y = MedWageEntry)) +
coord_flip()
#placement by university
courses %>%
left_join(institutions, by = "inst_id") %>%
filter(inst_type %in% c("State University", "Municipal University")) %>%
ggplot() +
geom_boxplot(mapping = aes(x = InstitutionName, y = Placement)) +
coord_flip()
#university by costs
courses %>%
left_join(institutions, by = "inst_id") %>%
filter(inst_type %in% c("State University", "Municipal University")) %>%
ggplot() +
geom_boxplot(mapping = aes(x = InstitutionName, y = TotalEstimatedCost)) +
coord_flip()
#find highest paying degrees
courses %>%
group_by(DegreeTitle) %>%
summarize(meanWage = mean(MedWage5yr, na.rm = TRUE)) %>%
arrange(desc(meanWage))
## # A tibble: 434 × 2
## DegreeTitle meanWage
## <chr> <dbl>
## 1 BUSINESS ANALYTICS 107783
## 2 ELECTRICAL & POWER TRANSMISSION 103802
## 3 ARCHITECTURAL ENGINEERING 103643
## 4 ELECTRONICS ENGINEERING TECHNOLOGY 102894
## 5 COMPUTER ENGINEERING 101775.
## 6 APPLIED COMPUTING 101604
## 7 ELECTRIC POWER AND DISTRIBUTION 101415
## 8 ELECTRICAL ENGINEERING 99578
## 9 COMPUTER SCIENCE 95220.
## 10 PETROLEUM ENGINEERING 94728
## # ℹ 424 more rows
#find which degrees/programs have highest wage vs cost
courses %>%
group_by(DegreeTitle) %>%
summarize(meanValue = mean(Med5YrValue, na.rm = TRUE), meanWage = mean(MedWage5yr, na.rm = TRUE), n = n()) %>%
filter(n > 4) %>%
arrange(desc(meanValue))
## # A tibble: 39 × 4
## DegreeTitle meanValue meanWage n
## <chr> <dbl> <dbl> <int>
## 1 COSMETOLOGY 6.07 38076 5
## 2 LIBERAL ARTS AND SCIENCES, GENERAL STUDIES AND HUMA… 5.50 43052. 10
## 3 WELDING TECHNOLOGY 5.40 51550 7
## 4 AA, AS, AGS DEGREES (TRANSFER DEGREES) 5.10 43240. 17
## 5 AUTOMOTIVE TECHNOLOGY 4.66 56143. 13
## 6 ELECTRICAL TECHNOLOGY 4.36 53807. 6
## 7 DIESEL TECHNOLOGY 4.24 54065. 5
## 8 PHYSICAL THERAPIST ASSISTANT 4.12 49155 5
## 9 LIBERAL STUDIES 4.05 41600. 9
## 10 SURGICAL TECHNOLOGY 3.93 48051. 5
## # ℹ 29 more rows
#compare wages by inst type
courses %>%
left_join(institutions, by = "inst_id") %>%
group_by(inst_type) %>%
filter(!is.na(inst_type)) %>%
ggplot() +
geom_boxplot(mapping = aes(x = inst_type, y = MedWageEntry)) +
coord_flip()
courses %>%
left_join(institutions, by = "inst_id") %>%
group_by(inst_type) %>%
filter(!is.na(inst_type)) %>%
ggplot() +
geom_boxplot(mapping = aes(x = inst_type, y = MedWage5yr)) +
coord_flip()
#compare only engineering degrees
courses %>%
left_join(institutions, by = "inst_id") %>%
filter(str_detect(DegreeTitle, "ENGINEERING")) %>%
filter(inst_type %in% c("State University", "Municipal University")) %>%
ggplot() +
geom_boxplot(mapping = aes(x = InstitutionName, y = MedWage5yr)) +
coord_flip()
